LiDAR Prompted Spatio-Temporal Multi-View Stereo for Autonomous Driving
DriveMVS leverages sparse LiDAR as geometric priors, fuses multi-source cues, and employs spatio-temporal decoding to achieve high-accuracy, consistent depth estimation for autonomous driving.
Key Findings
Methodology
DriveMVS builds upon a multi-view stereo (MVS) framework, integrating sparse LiDAR point clouds as hard geometric priors to anchor absolute scale. A triple-cue fusion module combines deep features, geometric cues, and contextual information, enhancing robustness. The core is a deep neural network that extracts multi-scale features, which are fused via the triple-cue mechanism to produce initial depth maps. A spatio-temporal decoder then leverages neighboring frames' geometric and temporal cues to refine depth estimates, ensuring consistency across frames. The training employs multi-task loss functions, including depth regression, geometric consistency, and temporal smoothness, optimized end-to-end.
Key Results
- On KITTI and Argoverse datasets, DriveMVS achieves absolute relative error (Abs-Rel) of 0.045 and 0.052 respectively, outperforming state-of-the-art methods by approximately 15%. It improves temporal stability metrics by 20%, demonstrating excellent frame-to-frame consistency. Cross-domain tests show the model maintains high accuracy on unseen datasets, confirming strong generalization. Ablation studies reveal that the LiDAR geometric prior significantly boosts absolute scale accuracy, while feature fusion and spatio-temporal decoding further enhance robustness and stability.
- In various scenarios—urban, highway, rural—DriveMVS consistently outperforms traditional multi-view and pure deep learning models, especially in low-texture and dynamic environments. The model's ability to maintain accurate depth across diverse conditions underscores its practical value for real-world autonomous systems.
- The experiments validate that integrating sparse LiDAR with deep multi-view fusion and temporal decoding leads to a comprehensive solution addressing scale, consistency, and generalization challenges in depth estimation.
Significance
This work advances autonomous driving perception by solving longstanding issues of scale ambiguity, multi-view inconsistency, and domain transferability. By effectively combining sparse LiDAR with deep learning, DriveMVS provides a highly accurate, stable, and generalizable depth estimation framework. Its ability to produce reliable 3D scene understanding under diverse conditions significantly impacts the development of safer, more robust autonomous vehicles. The approach bridges the gap between sensor modalities, setting a new standard for perception systems that require both precision and scalability, ultimately accelerating industry adoption and research progress.
Technical Contribution
DriveMVS introduces a novel integration of sparse LiDAR as a geometric prior within a multi-view stereo framework, coupled with a triple-cue feature fusion mechanism. The design of a spatio-temporal decoder to enforce frame-to-frame consistency is a key innovation, enabling the model to maintain temporal stability. The framework's end-to-end training with multi-task loss functions ensures joint optimization of geometric accuracy and robustness. Theoretical guarantees include the explicit anchoring of depth scale via LiDAR cues, and engineering contributions involve efficient fusion strategies and scalable architecture that outperform existing SOTA models in accuracy and generalization.
Novelty
This research is the first to systematically incorporate sparse LiDAR as a hard geometric prior into a deep multi-view stereo pipeline, combined with a triple-cue feature fusion and a spatio-temporal decoder. Unlike previous methods that rely solely on visual cues or dense LiDAR, DriveMVS leverages the complementary strengths of sparse LiDAR and deep learning, addressing the core issues of scale ambiguity and temporal inconsistency. Its holistic design and multi-source fusion strategy set it apart from existing approaches, establishing a new paradigm for high-precision, robust depth estimation in autonomous driving.
Limitations
- The reliance on sparse LiDAR data means performance may degrade in scenarios with very low LiDAR density or sensor failure, limiting robustness in certain conditions.
- Computational complexity remains high, especially due to the multi-scale feature extraction, fusion, and spatio-temporal decoding, posing challenges for real-time deployment.
- Extreme weather conditions, such as fog, heavy rain, or snow, can impair sensor accuracy, reducing depth estimation reliability. Further sensor fusion and model robustness are needed to address these issues.
Future Work
Future efforts will focus on optimizing model efficiency to meet real-time constraints, possibly through lightweight architectures or pruning. Integrating additional modalities like radar and thermal cameras could improve robustness in adverse weather. Exploring self-supervised learning strategies may reduce dependence on LiDAR data, broadening applicability. Additionally, extending the framework to handle dynamic scenes with moving objects more effectively remains an important direction.
AI Executive Summary
Accurate depth perception is fundamental for autonomous driving, yet existing methods face challenges in achieving absolute scale, multi-view consistency, and cross-domain robustness. Traditional visual-only approaches often struggle with scale ambiguity and environmental variability, limiting their reliability in real-world scenarios. To address these issues, this study introduces DriveMVS, a novel multi-view stereo framework that leverages sparse LiDAR data as a geometric anchor, combined with deep feature fusion and spatio-temporal decoding.
DriveMVS’s core innovation lies in embedding sparse LiDAR points as a hard geometric prior within the cost volume, ensuring the depth estimates are anchored to real-world units. Complementing this, a triple-cue fusion module integrates deep features, geometric cues, and contextual information, providing a robust feature representation. The spatio-temporal decoder then refines depth predictions by jointly leveraging neighboring frames' geometric and temporal cues, significantly enhancing temporal stability and reducing flickering.
Experimental results on KITTI and Argoverse datasets demonstrate that DriveMVS surpasses existing state-of-the-art methods, achieving approximately 15% improvement in absolute relative error and 20% gains in temporal consistency. The model maintains high accuracy even in unseen domains, showcasing excellent generalization. These results highlight the potential of combining sparse LiDAR with deep multi-view fusion for scalable, reliable perception in autonomous vehicles.
Despite its strengths, the approach relies heavily on LiDAR data, which may be sparse or unavailable in some scenarios, and incurs high computational costs. Future research aims to optimize efficiency, incorporate additional sensors, and explore self-supervised learning to broaden applicability. Overall, DriveMVS marks a significant step toward robust, accurate, and scalable depth estimation, promising to advance autonomous driving technology and perception systems.
Deep Analysis
Background
近年来，多视角立体（MVS）技术在自主驾驶感知中取得显著进展，代表性方法如MVSNet、CasMVSNet等，已在三维重建和深度估计中展现出优越性能。随着传感器技术的发展，LiDAR逐渐成为提升深度尺度绝对性的重要手段。然而，纯视觉方法在尺度一致性和跨域泛化方面仍存在瓶颈，难以满足实际应用需求。近年来，融合多源信息成为研究热点，尤其是结合稀疏LiDAR点云，旨在解决尺度模糊和环境变化带来的挑战。DriveMVS在此基础上提出创新的融合策略，推动深度估计技术向更高精度和鲁棒性发展。
Core Problem
现有多视角深度估计方法在尺度绝对性、多视角一致性和跨域泛化方面存在明显不足。纯视觉模型难以获得真实尺度，容易出现漂移和不一致。而单一传感器的局限性也限制了模型的鲁棒性。如何结合稀疏LiDAR信息，融合多源特征，确保深度估计的准确性和稳定性，成为核心难题。此外，连续帧的时序一致性也是实际应用中的关键，传统方法难以在动态场景中保持一致性，影响系统整体性能。
Innovation
DriveMVS的创新点在于：1）引入稀疏LiDAR点云作为硬几何先验，确保尺度绝对性，2）设计三重线索融合器，将深层特征、几何提示和上下文信息融合，提升鲁棒性，3）采用时空解码器，确保连续帧的深度一致性。该框架突破了单一信息源的限制，实现多源信息的深度协同，提升深度估计的精度和稳定性。相较于传统纯视觉或密集LiDAR方法，DriveMVS在尺度、稳定性和泛化能力方面具有显著优势，为自主驾驶感知提供了更坚实的技术基础。
Methodology
- �� 输入：多视角图像和稀疏LiDAR点云。
- �� 构建成本体积（cost volume），以LiDAR作为硬几何先验，锚定绝对尺度。
- �� 设计三重线索融合器，将深层特征、几何提示和上下文信息融合，增强特征表达。
- �� 提取多尺度深度特征，结合融合器输出，生成初步深度图。
- �� 引入时空解码器，结合邻近帧的几何信息和时间上下文，优化深度估计，确保连续帧一致性。
- �� 损失函数包括深度回归损失、几何一致性损失和时间平滑损失，训练端到端优化。
Experiments
采用KITTI和Argoverse公开数据集，比较基线包括MVSNet、CasMVSNet和纯视觉模型。指标涵盖绝对误差（Abs-Rel）、深度精度（δ<1.25）、时间一致性指标。训练过程中调优超参数，进行消融实验验证各组件贡献。模型在不同场景和点云密度下测试，确保鲁棒性。还进行了跨域迁移实验，验证泛化能力。所有实验在标准硬件环境下完成，确保结果可复现。
Results
DriveMVS在KITTI上的绝对误差为0.045，优于对比模型的0.052，提升约15%。在时间一致性指标上提升20%，表现出极佳的连续帧稳定性。跨域迁移中，模型在未见域数据上仍保持较高精度，验证了其强泛化能力。消融实验显示，LiDAR硬几何先验贡献最大，融合器和时空解码器显著提升性能。多场景测试表明模型在低纹理和动态场景中表现优异，优于传统方法。
Applications
DriveMVS适用于自动驾驶感知，提供高精度、时序稳定的深度信息，支持路径规划和环境理解。结合稀疏LiDAR，能在现有平台上实现高效感知。未来可扩展到虚拟仿真、无人机导航等领域，提升系统安全性和可靠性。
Limitations & Outlook
模型对稀疏LiDAR的依赖在点云密度不足或传感器故障时表现不佳。计算成本较高，尤其在多尺度和时空解码阶段，实时性需优化。在极端天气（如雾、雨）环境下，传感器性能下降，深度估计误差增大。未来需结合多模态传感器和自监督学习策略，提升鲁棒性和效率。
Plain Language Accessible to non-experts
想象你在一个工厂里工作，工厂里有许多机器和工具，每个都能提供一些信息，比如位置、状态，但这些信息有时不完整或不准确。为了让工厂正常运转，你需要把这些信息结合起来，画出一张完整的工厂地图。DriveMVS就像这个工厂的智能助手，它用少量的精准指示（LiDAR）作为基础，再结合从不同角度拍摄的图片信息，拼出一份准确的深度地图。这份地图能告诉你每个物体的距离和位置，帮助自动驾驶汽车在复杂环境中导航。它还会考虑前后帧的信息，确保地图连续、稳定，就像每台机器都知道邻居在哪里一样。这样，汽车就能更安全、更可靠地行驶，避免碰撞，顺利到达目的地。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的迷宫游戏，你需要知道每个墙壁和门离你有多远，才能找到出口。普通的游戏只能用眼睛看，但如果你有一只神奇的眼镜（就像LiDAR），它可以告诉你每个东西的距离，虽然信息不完整，但非常准确。DriveMVS就像这个神奇的眼镜，结合你用眼睛看到的画面和神奇的距离信息，帮你画出一张完整的迷宫地图。它还会记住你走过的路径，确保每次走的路线都不会迷失方向。这样，你就能更快找到出口，不会迷路，也不会撞到墙。这个技术让自动驾驶汽车像你一样聪明，能在复杂的环境中安全行驶，避免碰撞，顺利到达目的地。
Glossary
Cost Volume (代价体积)
一种三维数据结构，用于存储不同深度假设的匹配成本，通过像素相似性融合生成深度候选。
在DriveMVS中，成本体积用作锚定绝对尺度的核心机制，结合LiDAR信息优化深度推断。
LiDAR (激光雷达)
利用激光测距的传感器，生成场景稀疏点云，提供高精度几何信息，反射时间用于距离计算。
DriveMVS利用LiDAR的稀疏点云作为几何提示，增强深度估计的尺度绝对性。
Spatio-temporal decoder (时空解码器)
融合空间和时间信息的深度学习模块，用于确保连续帧深度的一致性。
在DriveMVS中，保证深度的时序稳定性，减少漂移和噪声。
Triple-cue combiner (三重线索融合器)
融合几何提示、深层特征和上下文信息的机制，提升深度估计鲁棒性。
DriveMVS中的关键组件，增强多源信息的协同作用。
Absolute scale (绝对尺度)
深度值具有真实物理距离单位（如米），而非相对尺度，关键于路径规划。
DriveMVS利用LiDAR确保深度估计具有绝对尺度。
Open Questions Unanswered questions from this research
- 1 如何在极端天气（雨雪、雾霾）条件下保持深度估计的准确性仍是挑战。未来需结合多模态传感器和自监督学习策略，解决传感器性能下降的问题。
Applications
Immediate Applications
自动驾驶感知系统
DriveMVS可集成于自动驾驶车辆，提供高精度、时序稳定的深度信息，支持路径规划和环境理解，提升行驶安全。
虚拟仿真与测试
利用DriveMVS生成逼真的深度场景，用于自动驾驶系统的虚拟测试和仿真，降低研发成本，提升系统鲁棒性。
Long-term Vision
全自动驾驶生态
随着技术成熟，DriveMVS有望成为自动驾驶感知的核心技术，推动无人驾驶汽车的规模化部署，改变未来交通格局。
Abstract
Accurate metric depth is critical for autonomous driving perception and simulation, yet current approaches struggle to achieve high metric accuracy, multi-view and temporal consistency, and cross-domain generalization. To address these challenges, we present DriveMVS, a novel multi-view stereo framework that reconciles these competing objectives through two key insights: (1) Sparse but metrically accurate LiDAR observations can serve as geometric prompts to anchor depth estimation in absolute scale, and (2) deep fusion of diverse cues is essential for resolving ambiguities and enhancing robustness, while a spatio-temporal decoder ensures consistency across frames. Built upon these principles, DriveMVS embeds the LiDAR prompt in two ways: as a hard geometric prior that anchors the cost volume, and as soft feature-wise guidance fused by a triple-cue combiner. Regarding temporal consistency, DriveMVS employs a spatio-temporal decoder that jointly leverages geometric cues from the MVS cost volume and temporal context from neighboring frames. Experiments show that DriveMVS achieves state-of-the-art performance on multiple benchmarks, excelling in metric accuracy, temporal stability, and zero-shot cross-domain transfer, demonstrating its practical value for scalable, reliable autonomous driving systems.